Artificial intelligence can do so many things for brand marketers. It can quickly analyze many megabits and even terabytes of data and give marketers recommendations. When it comes to content though, can AI create something out of nothing? Before I dig into the answer to that question, let's back up a bit and talk about content creation, human vs. machine. It's generally accepted that anyone who creates written or graphic content does so using one's own experience – including experiencing others' content.

Large corporations like Mastercard, Deloitte and Credit Suisse already use AI marketing tools to automate content creation, which allows them to boost productivity by creating content in less time. The Associated Press uses AI to write corporate earnings reports, freeing up reporters to do more in-depth reporting. You can apply an AI translation layer to the marketing tools you already use to quickly and efficiently write content that appeals to the new market, in a tone and style that is sensitive to the preferences of native speakers.

Donna McDaniel of Keller Independent School District in Texas was one of the first teachers in the world to use artificial intelligence-driven coaching for professional learning.

One of the biggest challenges that companies face as they try to improve their learning & development programs is time. With today's technology, L&D professionals can get granular data on how learners interact with content, what resonates and what does not. That information is a goldmine for refining L&D strategies and making training more efficient and effective. However, manually sifting through data and updating content can be too time consuming an endeavor to undertake. Artificial intelligence tools are the answer to this problem.

Staying on top of cybersecurity trends and threats is almost a full-time job. The industry expands every day as new applications come online for the internet of things, machine learning and artificial intelligence. But cybercriminals are innovating, too. Not a day passes without news of a major ransomware attack or phishing scheme. Businesses large and small now must learn the intricacies of data breaches and the dark web, negotiating with hackers, buying cyberinsurance and training employees on the best cyberhygiene.

Intelligence is the ability to gather information and apply it to the human experience. This was true when silicon was just a shiny rock, and it's true now that machines are becoming more intelligent. Businesses today need to deliver a different kind of intelligence: a high emotional quotient (EQ), which Harvard theorist Howard Gardner describes as the ability to understand what motivates another person and how to meet their needs. EQ (otherwise known as emotional intelligence) is mostly used to describe people--a friend's ability to empathize with a difficult situation, a manager adapting her approach to an employee's work style, or a salesperson relating to a potential buyer. It turns out that EQ is also important for businesses.

Machine learning has already captured the industry's attention and driven rapid changes in ad technology, which is the least it could do given the amount of hype it has received. What's even more fascinating, though, is that the pace of the ML revolution is only increasing, and the real change has barely begun. Smart use of ML is now a differentiator and competitive advantage, but it is about to become an absolute requirement to remaining relevant in ad tech. While there continues to be breakthroughs in core ML research, it is not the academic vanguard that is driving rapid change in our industry, but rather the broadening base of knowledge among nonspecialist engineers. Just a few years ago machine learning was largely restricted to a small group of experts -- a handful of Ph.D.s from a handful of top universities. The ML bottleneck for most ad tech companies was not technology but the recruiting and retention of this rare talent.

An issue across every sector, from market research to employee engagement and government relations, is how to truly understand large groups of people across political, geographical, and cultural divides and amplify their collective voice. This problem is intensified when challenging issues arise and sending out a survey doesn't provide the opportunity to discover what you don't know to ask. On the other hand, focus groups don't represent enough people to justify action. We've come a long way in learning how to better understand massive groups of people utilizing artificial intelligence (AI). This piece outlines key advantages of AI in market research resulting from my work with a number of organizations facing the challenge of transitioning from traditional market research to modern representative intelligence; that is intelligence capable of engaging, understanding and authentically representing massive groups of stakeholders (customer, employees, citizens, etc.).